Triple
T21534699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ida Vitale |
E531320
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Palabra dada
"Palabra dada" is a poetry collection by Uruguayan writer Ida Vitale that reflects her precise, reflective, and linguistically rich style.
|
E1488540
|
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: Palabra dada | Statement: [Ida Vitale, notableWork, Palabra dada]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Palabra dada Context triple: [Ida Vitale, notableWork, Palabra dada]
-
A.
Parola
Parola is a small town in Maharashtra, India, known for its historical fort and location within the Jalgaon district.
-
B.
Tres Palabras
"Tres Palabras" is a romantic bolero song, best known through classic Latin American interpretations and often associated with themes of longing and heartfelt love.
-
C.
Słówka
"Słówka" is a celebrated collection of satirical and witty verse by Polish writer and critic Tadeusz Boy-Żeleński.
-
D.
Hangman
Hangman is a classic word-guessing game in which players try to identify a hidden word by suggesting letters within a limited number of incorrect guesses.
-
E.
Hangman
Hangman is the call sign of Jake "Hangman" Seresin, a confident and skilled U.S. Navy fighter pilot character in the film "Top Gun: Maverick."
- 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: Palabra dada Triple: [Ida Vitale, notableWork, Palabra dada]
Generated description
"Palabra dada" is a poetry collection by Uruguayan writer Ida Vitale that reflects her precise, reflective, and linguistically rich style.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Palabra dada Target entity description: "Palabra dada" is a poetry collection by Uruguayan writer Ida Vitale that reflects her precise, reflective, and linguistically rich style.
-
A.
Parola
Parola is a small town in Maharashtra, India, known for its historical fort and location within the Jalgaon district.
-
B.
Tres Palabras
"Tres Palabras" is a romantic bolero song, best known through classic Latin American interpretations and often associated with themes of longing and heartfelt love.
-
C.
Słówka
"Słówka" is a celebrated collection of satirical and witty verse by Polish writer and critic Tadeusz Boy-Żeleński.
-
D.
Hangman
Hangman is a classic word-guessing game in which players try to identify a hidden word by suggesting letters within a limited number of incorrect guesses.
-
E.
Hangman
Hangman is the call sign of Jake "Hangman" Seresin, a confident and skilled U.S. Navy fighter pilot character in the film "Top Gun: Maverick."
- 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_69e0c45e5b8881908ac18fc2f493b114 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee9d0b9888819094e424d33c14d5d0 |
completed | April 26, 2026, 11:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09e8339c04819092968f7a7baba3f8 |
completed | May 17, 2026, 4:09 p.m. |
| NEDg | Description generation | batch_6a09e918bc80819093a8939e0eabf06f |
completed | May 17, 2026, 4:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09e98b27ec8190bacd93a48e4d5467 |
completed | May 17, 2026, 4:15 p.m. |
Created at: April 16, 2026, 6:27 p.m.