Triple
T23845399
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thumba Equatorial Rocket Launching Station |
E591105
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
TERLS
TERLS is an Indian rocket launch facility located near Thiruvananthapuram that has historically been used for sounding rocket and space research missions close to the magnetic equator.
|
E1605385
|
NE 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: TERLS | Statement: [Thumba Equatorial Rocket Launching Station, alsoKnownAs, TERLS]
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: TERLS Triple: [Thumba Equatorial Rocket Launching Station, alsoKnownAs, TERLS]
Generated description
TERLS is an Indian rocket launch facility located near Thiruvananthapuram that has historically been used for sounding rocket and space research missions close to the magnetic equator.
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_69e25d1de32c8190a907afe9c3d6cd6d |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1c88b59688190922d6bf329f08721 |
completed | April 29, 2026, 8:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0f69abd73881909ea879e885c3c35a |
completed | May 21, 2026, 8:23 p.m. |
| NEDg | Description generation | batch_6a0f6d40d1108190b4da250e40014008 |
completed | May 21, 2026, 8:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0f6e38cf648190b30122be93c70685 |
completed | May 21, 2026, 8:42 p.m. |
Created at: April 17, 2026, 8:09 p.m.