Triple
T36602909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marta Hallard |
E902966
|
entity |
| Predicate | relationshipToAlanGrant |
P204957
|
FINISHED |
| Object | close friend |
—
|
LITERAL FINISHED |
How this triple was built (2 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: close friend | Statement: [Marta Hallard, relationshipToAlanGrant, close friend]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToAlanGrant Context triple: [Marta Hallard, relationshipToAlanGrant, close friend]
-
A.
relationshipWithAlanHarper
Indicates that one entity has a specified type of personal or social relationship with Alan Harper.
-
B.
relationshipToSheldonCooper
Indicates the specific interpersonal or familial connection that an entity has to Sheldon Cooper.
-
C.
relationshipToShawnSpencer
Indicates the specific type of personal or social relationship an entity has with Shawn Spencer.
-
D.
relationshipWithDanaScully
Indicates having some form of personal or professional relationship with Dana Scully.
-
E.
relationshipToJoeBuck
Indicates the specific familial, social, or professional relationship that one entity has to the person Joe Buck.
- F. None of above. chosen
Provenance (4 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_69f76e66b7b88190848f7a3e1188915f |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037cad051c8190b28b354b89208574 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a0bf4b88190bdcfae9a14b51f0a |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82f8c88190bd77a086023ac0e1 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:11 p.m.