Triple
T19982208
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reservation Road |
E493843
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object |
Grace Learner
Grace Learner is a fictional character from the drama film "Reservation Road," which explores the emotional aftermath of a tragic hit-and-run accident.
|
E1403729
|
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: Grace Learner | Statement: [Reservation Road, hasCharacter, Grace Learner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grace Learner Context triple: [Reservation Road, hasCharacter, Grace Learner]
-
A.
Goodfellow
Goodfellow is a taxonomist recognized for formally describing and classifying bacterial groups such as the order Corynebacteriales.
-
B.
Selma Hacker
Selma Hacker is the gruff, deadpan court clerk on the sitcom "Night Court," known for her sardonic wit and no-nonsense demeanor.
-
C.
Sophia
Sophia is a person whose given name is used in the full name Sophia Chew Nicklin Dallas.
-
D.
Sophia
Sophia is the young, unhappily married woman at the center of the historical romance and art-themed drama in "Tulip Fever."
-
E.
Sophia
Sophia is the given name of Bamba Sophia Jindan Duleep Singh, a historical figure associated with the Sikh royal lineage.
- 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: Grace Learner Triple: [Reservation Road, hasCharacter, Grace Learner]
Generated description
Grace Learner is a fictional character from the drama film "Reservation Road," which explores the emotional aftermath of a tragic hit-and-run accident.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Grace Learner Target entity description: Grace Learner is a fictional character from the drama film "Reservation Road," which explores the emotional aftermath of a tragic hit-and-run accident.
-
A.
Goodfellow
Goodfellow is a taxonomist recognized for formally describing and classifying bacterial groups such as the order Corynebacteriales.
-
B.
Selma Hacker
Selma Hacker is the gruff, deadpan court clerk on the sitcom "Night Court," known for her sardonic wit and no-nonsense demeanor.
-
C.
Sophia
Sophia is a person whose given name is used in the full name Sophia Chew Nicklin Dallas.
-
D.
Sophia
Sophia is the young, unhappily married woman at the center of the historical romance and art-themed drama in "Tulip Fever."
-
E.
Sophia
Sophia is the given name of Bamba Sophia Jindan Duleep Singh, a historical figure associated with the Sikh royal lineage.
- 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65d13a8a88190bf5f4f697793f4c9 |
completed | April 20, 2026, 5:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07fdddbf5c81908e194fb1d6c31e15 |
completed | May 16, 2026, 5:17 a.m. |
| NEDg | Description generation | batch_6a07fea23a988190832115346f8889ca |
completed | May 16, 2026, 5:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07ff4a59608190a17f30f60c879efc |
completed | May 16, 2026, 5:23 a.m. |
Created at: April 11, 2026, 3:28 p.m.