Triple
T20586892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Colonel Hathi’s March |
E505810
|
entity |
| Predicate | performer |
P1363
|
FINISHED |
| Object |
Colonel Hathi
Colonel Hathi is a pompous but well-meaning elephant military leader from Disney’s animated film "The Jungle Book."
|
E1438096
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Colonel Hathi | Statement: [Colonel Hathi’s March, performer, Colonel Hathi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Colonel Hathi Context triple: [Colonel Hathi’s March, performer, Colonel Hathi]
-
A.
Colonel Rol
Colonel Rol was the nom de guerre of French Resistance leader Henri Rol-Tanguy, who played a key role in the liberation of Paris during World War II.
-
B.
Colonel Haki
Colonel Haki is a shrewd and persistent Turkish police officer who aids in unraveling the criminal mystery at the heart of Eric Ambler’s novel "The Mask of Dimitrios."
-
C.
Colonel Bob
Colonel Bob is a prominent peak in Washington State’s Olympic Peninsula, known for its rugged wilderness setting and panoramic views.
-
D.
Colonel Howard
Colonel Howard is a fictional military officer character featured in the film "The Pilot."
-
E.
Colonel Bobi
Colonel Bobi is a ruthless and power-hungry military officer who becomes the central figure in a coup plot in Frederick Forsyth’s novel "The Dogs of War."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Colonel Hathi Triple: [Colonel Hathi’s March, performer, Colonel Hathi]
Generated description
Colonel Hathi is a pompous but well-meaning elephant military leader from Disney’s animated film "The Jungle Book."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Colonel Hathi Target entity description: Colonel Hathi is a pompous but well-meaning elephant military leader from Disney’s animated film "The Jungle Book."
-
A.
Colonel Rol
Colonel Rol was the nom de guerre of French Resistance leader Henri Rol-Tanguy, who played a key role in the liberation of Paris during World War II.
-
B.
Colonel Haki
Colonel Haki is a shrewd and persistent Turkish police officer who aids in unraveling the criminal mystery at the heart of Eric Ambler’s novel "The Mask of Dimitrios."
-
C.
Colonel Bob
Colonel Bob is a prominent peak in Washington State’s Olympic Peninsula, known for its rugged wilderness setting and panoramic views.
-
D.
Colonel Howard
Colonel Howard is a fictional military officer character featured in the film "The Pilot."
-
E.
Colonel Bobi
Colonel Bobi is a ruthless and power-hungry military officer who becomes the central figure in a coup plot in Frederick Forsyth’s novel "The Dogs of War."
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69e0b4b9669c8190b8e81fc72817d42c |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a977fb18819085fee5cf5d45c1b0 |
completed | April 20, 2026, 10:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a08acf0a968819082b06b0fdc6a6ee5 |
completed | May 16, 2026, 5:44 p.m. |
| NEDg | Description generation | batch_6a08ad559464819089921d7a7e04bb57 |
completed | May 16, 2026, 5:45 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a08add9f570819080d8baf1af34201e |
completed | May 16, 2026, 5:48 p.m. |
Created at: April 16, 2026, 11:40 a.m.